import os

Below code to train CNN classification model

import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import regularizers,optimizers
from keras.models import Sequential
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from keras.layers import Conv2D, MaxPooling2D,Activation
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import pickle              # For saving models to disk and loading saved models
import os
import zipfile
import PIL 
from PIL import Image, ImageDraw, ImageEnhance
import pathlib
import cv2 as cv2
import imgaug as ia
ia.seed(1)
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
from imgaug import augmenters as iaa 
import imageio
import re
import shutil

C:\Users\uprak\anaconda3\envs\tflow\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
C:\Users\uprak\anaconda3\envs\tflow\lib\site-packages\numpy\.libs\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll
C:\Users\uprak\anaconda3\envs\tflow\lib\site-packages\numpy\.libs\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll
  warnings.warn("loaded more than 1 DLL from .libs:"

# Initializing the random number generator
import random
random.seed(0)

# Ignoring the warnings
import warnings
warnings.filterwarnings("ignore")

os.getcwd()

'C:\\Users\\uprak\\capstone'

#path = os.getcwd()
path = 'C:\\yolo_result'
os.chdir(path)

ls

 Volume in drive C is Windows
 Volume Serial Number is D87D-3DAA

 Directory of C:\yolo_result

19-01-2022  00:16    <DIR>          .
19-01-2022  00:16    <DIR>          ..
18-01-2022  23:58           307,619 yolo_car_classification12_events.out.tfevents.1642401324.tf-gpu-1.22030.0
18-01-2022  23:58               372 yolo_car_classification12_hyp.yaml
19-01-2022  00:06           258,111 yolo_car_classification12_labels (1).jpg
19-01-2022  00:05           258,111 yolo_car_classification12_labels.jpg
19-01-2022  00:06           230,888 yolo_car_classification12_labels_correlogram.jpg
18-01-2022  23:59               709 yolo_car_classification12_opt.yaml
18-01-2022  23:59            36,456 yolo_car_classification12_results.csv
19-01-2022  00:05           180,294 yolo_car_classification12_train_batch0.jpg
19-01-2022  00:05           177,023 yolo_car_classification12_train_batch1.jpg
19-01-2022  00:05           172,418 yolo_car_classification12_train_batch2.jpg
              10 File(s)      1,622,001 bytes
               2 Dir(s)  58,253,090,816 bytes free

%cd ..

C:\

path

'C:\\Users\\uprak\\capstone'

car_img_path = os.path.join(path,'Car Images')
car_ann_path = os.path.join(path,'Annotations')
train_img_dir = os.path.join(car_img_path,'Train Images')
test_img_dir =  os.path.join(car_img_path,'Test Images')

def load_images_from_folder(folder):
    file_path = {}
    count = 0
    for folders in os.listdir(folder):
        #print(folders)
        if folders == '.DS_Store':
            pass
        else:
            foldername = os.path.join(folder,folders)
            #print(foldername)
            for filename in os.listdir(foldername):
                count = count+1
                filepath = os.path.join(foldername,filename)
                #file_name.append(filename)
                file_path[filename]=filepath
                #print(filename)
    return file_path

#Following function to plot the epochs vs loss and epoch vs metrics graph for training and validation results
def plot_curves(history,metrics):
    nrows = 1
    ncols = 2
    fig = plt.figure(figsize = (10,5))
    for idx,key in enumerate(metrics):
        ax = fig.add_subplot(nrows,ncols,idx+1)
        plt.plot(history.history[key])
        plt.plot(history.history['val_{}'.format(key)])
        plt.title('model {}'.format(key))
        plt.ylabel(key)
        plt.xlabel('epoch')
        plt.legend(['train','validation'],loc='upper left')

def resize_image(image_list,resize_path,image_size=224):
    for img in image_list:
        #print(img)
        im = cv2.imread(img,cv2.COLOR_BGR2RGB)
        old_size = im.shape[:2]  # old_size is in (height, width) format
        ratio = float(image_size) / max(old_size)
        new_size = tuple([int(x * ratio) for x in old_size])
        # new_size should be in (width, height) format
        im = cv2.resize(im, (new_size[1], new_size[0]))
        delta_w = image_size - new_size[1]
        delta_h = image_size - new_size[0]
        top, bottom = delta_h // 2, delta_h - (delta_h // 2)
        left, right = delta_w // 2, delta_w - (delta_w // 2)
        color = [0, 0, 0]
        new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
                                    value=color)
        #print(new_im.shape)
        img_path = img.split('\\')
        #print(img_path[-1])
        r_path = os.path.join(resize_path,img_path[-1])
        print(r_path)
        cv2.imwrite(r_path, new_im)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
#img_list = list(df_train["Image_Path"])
#resize_image(img_list,"resize/")
#img_list = list(df_test["Image_Path"])
#resize_image(img_list,"resize_test/")        

def compress_pca(image,PCA_n_component=50):
    img = cv2.imread(image,cv2.COLOR_BGR2RGB)
    try:
        if(len(img.shape) < 3):
            print(len(img.shape))
            print("\n file_path: ",image)
            gray = cv2.imread(image,0)
            blue=red=green=gray
        else:
            red,green,blue = cv2.split(img)
    except:
        #gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        Print("Exception encountered")
        #return img

    df_blue = blue/255
    df_green = green/255
    df_red = red/255
    pca_b = PCA(n_components=PCA_n_component)
    pca_b.fit(df_blue)
    trans_pca_b = pca_b.transform(df_blue)
    pca_g = PCA(n_components=PCA_n_component)
    pca_g.fit(df_green)
    trans_pca_g = pca_g.transform(df_green)
    pca_r = PCA(n_components=PCA_n_component)
    pca_r.fit(df_red)
    trans_pca_r = pca_r.transform(df_red)
    b_arr = pca_b.inverse_transform(trans_pca_b)
    g_arr = pca_g.inverse_transform(trans_pca_g)
    r_arr = pca_r.inverse_transform(trans_pca_r)
    img_reduced= (cv2.merge((r_arr, g_arr, b_arr)))
    
    
    return img_reduced

def compress_image(image_list,resize_path,PCA_n_component=50):
    for img in image_list:
        #print(img)
        im = compress_pca(img,40)
        img_path = img.split('\\')
        #print(img_path[-1])
        if img_path[-1] == "00001.jpg":
            plt.imshow(im)
        r_path = os.path.join(resize_path,img_path[-1])
        #print(r_path)
        im = cv2.convertScaleAbs(im, alpha=(255.0))
        if not cv2.imwrite(r_path, im):
            raise Exception("Could not write image")
        cv2.waitKey()
        cv2.destroyAllWindows()
    return
#img_list = list(df_train["Resize_Img_Path"])
#compress_image(img_list,"resize_compress/")
#img_list = list(df_test["Resize_Img_Path"])
#compress_image(img_list,"resize_test_compress/")       

df_train = pd.read_csv("Annotations/train.csv")
df_test = pd.read_csv("Annotations/test.csv")

train_images_path = load_images_from_folder(train_img_dir)
test_images_path = load_images_from_folder(test_img_dir)
df_train['Image_Path'] = df_train['Image_Name'].apply(lambda x:train_images_path[x])
df_test['Image_Path'] = df_test['Image_Name'].apply(lambda x:test_images_path[x])
#df_train_ann['Car_Name'] = df_train_ann['Image_class'].apply(lambda x:car_dict[int(x)])

df_train.sample(5)

	Image_Name	Start_x	Start_y	End_x	End_y	Image_class	Image_Path	Car_Name	Img_H	Img_W	Resize_Img_Path	Car_Type	Model	Year
2337	02338.jpg	0.112727	0.218579	0.665455	0.841530	28	C:\Users\uprak\capstone\Car Images\Train Image...	BMW 1 Series Coupe 2012	183	275	/content/drive/My Drive/capstone/Car Images/tr...	Coupe	BMW	2012
518	00519.jpg	0.026515	0.196970	0.962121	0.893939	162	C:\Users\uprak\capstone\Car Images\Train Image...	Mercedes-Benz C-Class Sedan 2012	198	264	/content/drive/My Drive/capstone/Car Images/tr...	Sedan	Mercedes-Benz	2012
5544	05545.jpg	0.091667	0.266106	0.938333	0.896359	3	C:\Users\uprak\capstone\Car Images\Train Image...	Acura TL Sedan 2012	357	600	/content/drive/My Drive/capstone/Car Images/tr...	Sedan	Acura	2012
460	00461.jpg	0.173437	0.005556	0.864062	0.972222	179	C:\Users\uprak\capstone\Car Images\Train Image...	Spyker C8 Convertible 2009	360	640	/content/drive/My Drive/capstone/Car Images/tr...	Convertible	Spyker	2009
5197	05198.jpg	0.305000	0.287054	0.790000	0.744841	140	C:\Users\uprak\capstone\Car Images\Train Image...	Hyundai Azera Sedan 2012	533	800	/content/drive/My Drive/capstone/Car Images/tr...	Sedan	Hyundai	2012

df_car_name = pd.read_csv("Car names and make.csv",names=['Car Name'])
df_car_name

	Car Name
0	AM General Hummer SUV 2000
1	Acura RL Sedan 2012
2	Acura TL Sedan 2012
3	Acura TL Type-S 2008
4	Acura TSX Sedan 2012
...	...
191	Volkswagen Beetle Hatchback 2012
192	Volvo C30 Hatchback 2012
193	Volvo 240 Sedan 1993
194	Volvo XC90 SUV 2007
195	smart fortwo Convertible 2012

196 rows × 1 columns

count = 0
car_dict = {}
for car_name in df_car_name['Car Name'].values:
    if car_name == 'Ram C/V Cargo Van Minivan 2012':
        car_name = 'Ram C-V Cargo Van Minivan 2012'
    car_dict[count+1] = car_name
    count=count+1
car_dict

{1: 'AM General Hummer SUV 2000',
 2: 'Acura RL Sedan 2012',
 3: 'Acura TL Sedan 2012',
 4: 'Acura TL Type-S 2008',
 5: 'Acura TSX Sedan 2012',
 6: 'Acura Integra Type R 2001',
 7: 'Acura ZDX Hatchback 2012',
 8: 'Aston Martin V8 Vantage Convertible 2012',
 9: 'Aston Martin V8 Vantage Coupe 2012',
 10: 'Aston Martin Virage Convertible 2012',
 11: 'Aston Martin Virage Coupe 2012',
 12: 'Audi RS 4 Convertible 2008',
 13: 'Audi A5 Coupe 2012',
 14: 'Audi TTS Coupe 2012',
 15: 'Audi R8 Coupe 2012',
 16: 'Audi V8 Sedan 1994',
 17: 'Audi 100 Sedan 1994',
 18: 'Audi 100 Wagon 1994',
 19: 'Audi TT Hatchback 2011',
 20: 'Audi S6 Sedan 2011',
 21: 'Audi S5 Convertible 2012',
 22: 'Audi S5 Coupe 2012',
 23: 'Audi S4 Sedan 2012',
 24: 'Audi S4 Sedan 2007',
 25: 'Audi TT RS Coupe 2012',
 26: 'BMW ActiveHybrid 5 Sedan 2012',
 27: 'BMW 1 Series Convertible 2012',
 28: 'BMW 1 Series Coupe 2012',
 29: 'BMW 3 Series Sedan 2012',
 30: 'BMW 3 Series Wagon 2012',
 31: 'BMW 6 Series Convertible 2007',
 32: 'BMW X5 SUV 2007',
 33: 'BMW X6 SUV 2012',
 34: 'BMW M3 Coupe 2012',
 35: 'BMW M5 Sedan 2010',
 36: 'BMW M6 Convertible 2010',
 37: 'BMW X3 SUV 2012',
 38: 'BMW Z4 Convertible 2012',
 39: 'Bentley Continental Supersports Conv. Convertible 2012',
 40: 'Bentley Arnage Sedan 2009',
 41: 'Bentley Mulsanne Sedan 2011',
 42: 'Bentley Continental GT Coupe 2012',
 43: 'Bentley Continental GT Coupe 2007',
 44: 'Bentley Continental Flying Spur Sedan 2007',
 45: 'Bugatti Veyron 16.4 Convertible 2009',
 46: 'Bugatti Veyron 16.4 Coupe 2009',
 47: 'Buick Regal GS 2012',
 48: 'Buick Rainier SUV 2007',
 49: 'Buick Verano Sedan 2012',
 50: 'Buick Enclave SUV 2012',
 51: 'Cadillac CTS-V Sedan 2012',
 52: 'Cadillac SRX SUV 2012',
 53: 'Cadillac Escalade EXT Crew Cab 2007',
 54: 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012',
 55: 'Chevrolet Corvette Convertible 2012',
 56: 'Chevrolet Corvette ZR1 2012',
 57: 'Chevrolet Corvette Ron Fellows Edition Z06 2007',
 58: 'Chevrolet Traverse SUV 2012',
 59: 'Chevrolet Camaro Convertible 2012',
 60: 'Chevrolet HHR SS 2010',
 61: 'Chevrolet Impala Sedan 2007',
 62: 'Chevrolet Tahoe Hybrid SUV 2012',
 63: 'Chevrolet Sonic Sedan 2012',
 64: 'Chevrolet Express Cargo Van 2007',
 65: 'Chevrolet Avalanche Crew Cab 2012',
 66: 'Chevrolet Cobalt SS 2010',
 67: 'Chevrolet Malibu Hybrid Sedan 2010',
 68: 'Chevrolet TrailBlazer SS 2009',
 69: 'Chevrolet Silverado 2500HD Regular Cab 2012',
 70: 'Chevrolet Silverado 1500 Classic Extended Cab 2007',
 71: 'Chevrolet Express Van 2007',
 72: 'Chevrolet Monte Carlo Coupe 2007',
 73: 'Chevrolet Malibu Sedan 2007',
 74: 'Chevrolet Silverado 1500 Extended Cab 2012',
 75: 'Chevrolet Silverado 1500 Regular Cab 2012',
 76: 'Chrysler Aspen SUV 2009',
 77: 'Chrysler Sebring Convertible 2010',
 78: 'Chrysler Town and Country Minivan 2012',
 79: 'Chrysler 300 SRT-8 2010',
 80: 'Chrysler Crossfire Convertible 2008',
 81: 'Chrysler PT Cruiser Convertible 2008',
 82: 'Daewoo Nubira Wagon 2002',
 83: 'Dodge Caliber Wagon 2012',
 84: 'Dodge Caliber Wagon 2007',
 85: 'Dodge Caravan Minivan 1997',
 86: 'Dodge Ram Pickup 3500 Crew Cab 2010',
 87: 'Dodge Ram Pickup 3500 Quad Cab 2009',
 88: 'Dodge Sprinter Cargo Van 2009',
 89: 'Dodge Journey SUV 2012',
 90: 'Dodge Dakota Crew Cab 2010',
 91: 'Dodge Dakota Club Cab 2007',
 92: 'Dodge Magnum Wagon 2008',
 93: 'Dodge Challenger SRT8 2011',
 94: 'Dodge Durango SUV 2012',
 95: 'Dodge Durango SUV 2007',
 96: 'Dodge Charger Sedan 2012',
 97: 'Dodge Charger SRT-8 2009',
 98: 'Eagle Talon Hatchback 1998',
 99: 'FIAT 500 Abarth 2012',
 100: 'FIAT 500 Convertible 2012',
 101: 'Ferrari FF Coupe 2012',
 102: 'Ferrari California Convertible 2012',
 103: 'Ferrari 458 Italia Convertible 2012',
 104: 'Ferrari 458 Italia Coupe 2012',
 105: 'Fisker Karma Sedan 2012',
 106: 'Ford F-450 Super Duty Crew Cab 2012',
 107: 'Ford Mustang Convertible 2007',
 108: 'Ford Freestar Minivan 2007',
 109: 'Ford Expedition EL SUV 2009',
 110: 'Ford Edge SUV 2012',
 111: 'Ford Ranger SuperCab 2011',
 112: 'Ford GT Coupe 2006',
 113: 'Ford F-150 Regular Cab 2012',
 114: 'Ford F-150 Regular Cab 2007',
 115: 'Ford Focus Sedan 2007',
 116: 'Ford E-Series Wagon Van 2012',
 117: 'Ford Fiesta Sedan 2012',
 118: 'GMC Terrain SUV 2012',
 119: 'GMC Savana Van 2012',
 120: 'GMC Yukon Hybrid SUV 2012',
 121: 'GMC Acadia SUV 2012',
 122: 'GMC Canyon Extended Cab 2012',
 123: 'Geo Metro Convertible 1993',
 124: 'HUMMER H3T Crew Cab 2010',
 125: 'HUMMER H2 SUT Crew Cab 2009',
 126: 'Honda Odyssey Minivan 2012',
 127: 'Honda Odyssey Minivan 2007',
 128: 'Honda Accord Coupe 2012',
 129: 'Honda Accord Sedan 2012',
 130: 'Hyundai Veloster Hatchback 2012',
 131: 'Hyundai Santa Fe SUV 2012',
 132: 'Hyundai Tucson SUV 2012',
 133: 'Hyundai Veracruz SUV 2012',
 134: 'Hyundai Sonata Hybrid Sedan 2012',
 135: 'Hyundai Elantra Sedan 2007',
 136: 'Hyundai Accent Sedan 2012',
 137: 'Hyundai Genesis Sedan 2012',
 138: 'Hyundai Sonata Sedan 2012',
 139: 'Hyundai Elantra Touring Hatchback 2012',
 140: 'Hyundai Azera Sedan 2012',
 141: 'Infiniti G Coupe IPL 2012',
 142: 'Infiniti QX56 SUV 2011',
 143: 'Isuzu Ascender SUV 2008',
 144: 'Jaguar XK XKR 2012',
 145: 'Jeep Patriot SUV 2012',
 146: 'Jeep Wrangler SUV 2012',
 147: 'Jeep Liberty SUV 2012',
 148: 'Jeep Grand Cherokee SUV 2012',
 149: 'Jeep Compass SUV 2012',
 150: 'Lamborghini Reventon Coupe 2008',
 151: 'Lamborghini Aventador Coupe 2012',
 152: 'Lamborghini Gallardo LP 570-4 Superleggera 2012',
 153: 'Lamborghini Diablo Coupe 2001',
 154: 'Land Rover Range Rover SUV 2012',
 155: 'Land Rover LR2 SUV 2012',
 156: 'Lincoln Town Car Sedan 2011',
 157: 'MINI Cooper Roadster Convertible 2012',
 158: 'Maybach Landaulet Convertible 2012',
 159: 'Mazda Tribute SUV 2011',
 160: 'McLaren MP4-12C Coupe 2012',
 161: 'Mercedes-Benz 300-Class Convertible 1993',
 162: 'Mercedes-Benz C-Class Sedan 2012',
 163: 'Mercedes-Benz SL-Class Coupe 2009',
 164: 'Mercedes-Benz E-Class Sedan 2012',
 165: 'Mercedes-Benz S-Class Sedan 2012',
 166: 'Mercedes-Benz Sprinter Van 2012',
 167: 'Mitsubishi Lancer Sedan 2012',
 168: 'Nissan Leaf Hatchback 2012',
 169: 'Nissan NV Passenger Van 2012',
 170: 'Nissan Juke Hatchback 2012',
 171: 'Nissan 240SX Coupe 1998',
 172: 'Plymouth Neon Coupe 1999',
 173: 'Porsche Panamera Sedan 2012',
 174: 'Ram C-V Cargo Van Minivan 2012',
 175: 'Rolls-Royce Phantom Drophead Coupe Convertible 2012',
 176: 'Rolls-Royce Ghost Sedan 2012',
 177: 'Rolls-Royce Phantom Sedan 2012',
 178: 'Scion xD Hatchback 2012',
 179: 'Spyker C8 Convertible 2009',
 180: 'Spyker C8 Coupe 2009',
 181: 'Suzuki Aerio Sedan 2007',
 182: 'Suzuki Kizashi Sedan 2012',
 183: 'Suzuki SX4 Hatchback 2012',
 184: 'Suzuki SX4 Sedan 2012',
 185: 'Tesla Model S Sedan 2012',
 186: 'Toyota Sequoia SUV 2012',
 187: 'Toyota Camry Sedan 2012',
 188: 'Toyota Corolla Sedan 2012',
 189: 'Toyota 4Runner SUV 2012',
 190: 'Volkswagen Golf Hatchback 2012',
 191: 'Volkswagen Golf Hatchback 1991',
 192: 'Volkswagen Beetle Hatchback 2012',
 193: 'Volvo C30 Hatchback 2012',
 194: 'Volvo 240 Sedan 1993',
 195: 'Volvo XC90 SUV 2007',
 196: 'smart fortwo Convertible 2012'}

car_names = list(df_car_name['Car Name'].values)
car_types = []
car_year = []
car_model = []
for cars in car_names:
    carname = cars.split(' ')
    model_word_length = len(carname) - 3 #Remove 1 each for name od car company, type of car and model year
    car_types.append(carname[len(carname)-2])
    car_year.append(carname[len(carname)-1])
    car_model.append(carname[0])

df_car_name['Car_Type'] = car_types
df_car_name['Model'] = car_model
df_car_name['Year'] = car_year
df_car_name.sample(10)

	Car Name	Car_Type	Model	Year
90	Dodge Dakota Club Cab 2007	Cab	Dodge	2007
29	BMW 3 Series Wagon 2012	Wagon	BMW	2012
20	Audi S5 Convertible 2012	Convertible	Audi	2012
181	Suzuki Kizashi Sedan 2012	Sedan	Suzuki	2012
145	Jeep Wrangler SUV 2012	SUV	Jeep	2012
32	BMW X6 SUV 2012	SUV	BMW	2012
27	BMW 1 Series Coupe 2012	Coupe	BMW	2012
168	Nissan NV Passenger Van 2012	Van	Nissan	2012
149	Lamborghini Reventon Coupe 2008	Coupe	Lamborghini	2008
67	Chevrolet TrailBlazer SS 2009	SS	Chevrolet	2009

Sedan = ['Sedan','GS','Type-S','SRT-8']
Hatchback = ['Hatchback','Abarth']
Coupe = ['Coupe','IPL','XKR','Z06','ZR1','R','SRT8','Superleggera']
SUV = ['SUV','SS']
Van = ['Van','Minivan','Wagon','Cab','SuperCab']
df_car_name['Car_Type'].replace(to_replace=Sedan,value='Sedan',inplace=True) 
df_car_name['Car_Type'].replace(to_replace=Hatchback,value='Hatchback',inplace=True) 
df_car_name['Car_Type'].replace(to_replace=Coupe,value='Coupe',inplace=True)
df_car_name['Car_Type'].replace(to_replace=SUV,value='SUV',inplace=True)
df_car_name['Car_Type'].replace(to_replace=Van,value='Van',inplace=True)
df_car_name.sample(5)

	Car Name	Car_Type	Model	Year
0	AM General Hummer SUV 2000	SUV	AM	2000
40	Bentley Mulsanne Sedan 2011	Sedan	Bentley	2011
93	Dodge Durango SUV 2012	SUV	Dodge	2012
111	Ford GT Coupe 2006	Coupe	Ford	2006
129	Hyundai Veloster Hatchback 2012	Hatchback	Hyundai	2012

df_car_name['Car Name'].replace(to_replace=['Ram C/V Cargo Van Minivan 2012'],value='Ram C-V Cargo Van Minivan 2012',inplace=True)

df_visualize = df_train[['Image_Path','Car_Name','Start_x','Start_y','End_x','End_y']].sample(10)

df_visualize['Bbox'] = df_visualize.apply(lambda x:list([float(x['Start_x']),float(x['Start_y']),float(x['End_x']),float(x['End_y'])]),axis=1)
df_visualize

	Image_Path	Car_Name	Start_x	Start_y	End_x	End_y	Bbox
2682	C:\Users\uprak\capstone\Car Images\Train Image...	Chevrolet Monte Carlo Coupe 2007	0.046332	0.237113	0.945946	0.783505	[0.0463320463320463, 0.2371134020618557, 0.945...
7191	C:\Users\uprak\capstone\Car Images\Train Image...	Chevrolet Avalanche Crew Cab 2012	0.112500	0.260417	0.929688	0.750000	[0.1125, 0.2604166666666667, 0.9296875, 0.75]
1357	C:\Users\uprak\capstone\Car Images\Train Image...	Chevrolet Camaro Convertible 2012	0.016667	0.002801	0.938333	0.857143	[0.0166666666666666, 0.0028011204481792, 0.938...
2996	C:\Users\uprak\capstone\Car Images\Train Image...	Volkswagen Beetle Hatchback 2012	0.150667	0.103412	0.918667	0.904051	[0.1506666666666666, 0.103411513859275, 0.9186...
1474	C:\Users\uprak\capstone\Car Images\Train Image...	Hyundai Elantra Touring Hatchback 2012	0.016667	0.135734	0.991667	0.861496	[0.0166666666666666, 0.1357340720221606, 0.991...
6977	C:\Users\uprak\capstone\Car Images\Train Image...	Dodge Ram Pickup 3500 Quad Cab 2009	0.040039	0.136719	0.950195	0.789062	[0.0400390625, 0.13671875, 0.9501953125, 0.789...
4275	C:\Users\uprak\capstone\Car Images\Train Image...	Spyker C8 Convertible 2009	0.068000	0.270270	0.948000	0.720721	[0.068, 0.2702702702702703, 0.948, 0.720720720...
633	C:\Users\uprak\capstone\Car Images\Train Image...	Aston Martin V8 Vantage Coupe 2012	0.050000	0.184524	0.953333	0.880952	[0.05, 0.1845238095238095, 0.9533333333333334,...
6919	C:\Users\uprak\capstone\Car Images\Train Image...	Lincoln Town Car Sedan 2011	0.010937	0.341667	0.993750	0.712500	[0.0109375, 0.3416666666666667, 0.99375, 0.7125]
2935	C:\Users\uprak\capstone\Car Images\Train Image...	BMW Z4 Convertible 2012	0.092500	0.112195	0.917500	0.946341	[0.0925, 0.1121951219512195, 0.9175, 0.9463414...

image_list = list(df_visualize['Image_Path'])
class_list = list(df_visualize['Car_Name'])
bbox_list = list(df_visualize['Bbox'])

BOX_COLOR = (255, 0, 0) # Red
TEXT_COLOR = (0, 0, 0) # White
#img_path = "/content/drive/My Drive/capstone/Car Images/train_img_300_resized/"

def visualize_bbox(img, bbox, class_name, color=BOX_COLOR, thickness=2):
    """Visualizes a single bounding box on the image"""
    x_min, y_min, x_max, y_max = map(int,bbox)
    cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=BOX_COLOR, thickness=thickness)
    ((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)    
    cv2.rectangle(img, (x_min, (y_min - int(0.3 * text_height))), (x_min + text_width, y_min), BOX_COLOR, -1)
    cv2.putText(
        img,
        text=class_name,
        org=(x_min, y_min - int(0.3 * text_height)),
        fontFace=cv2.FONT_HERSHEY_SIMPLEX,
        fontScale=0.35, 
        color=TEXT_COLOR, 
        lineType=cv2.LINE_AA,
    )
    return img


def visualize(image_list, bbox_list, class_list):
    count = 0
    image_final = []
    for image_name in image_list:
        print(image_name)
        #filename = os.path.join(img_path,image_name)
        #print(filename)
        image = cv2.imread(image_name,cv2.COLOR_BGR2RGB)
        #print(image)
        img = image.copy()
        h,w,_ = img.shape
        #img = (cv.resize(img,(224,224)))
        #print(img)
        bbox = bbox_list[count]
        bbox[0] = bbox[0]*w
        bbox[1] = bbox[1]*h
        bbox[2] = bbox[2]*w
        bbox[3] = bbox[3]*h
        #print(bbox)
        class_name = class_list[count]
        #print(class_name)
        img = visualize_bbox(img, bbox, str(class_name))
        image_final.append(img)
        count = count + 1
    #plt.figure(figsize=(50, 50))
    fig, axs = plt.subplots(int(count), 1,figsize=(70, 70))
    axs = axs.flatten()
    for img,ax in zip(image_final,axs):
        ax.imshow(img)
    plt.show()

visualize(image_list,bbox_list,class_list)

C:\Users\uprak\capstone\Car Images\Train Images\GMC Savana Van 2012\08131.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Land Rover Range Rover SUV 2012\02090.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Aston Martin V8 Vantage Coupe 2012\07471.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Jeep Liberty SUV 2012\06331.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Mitsubishi Lancer Sedan 2012\07494.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Lincoln Town Car Sedan 2011\07847.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Isuzu Ascender SUV 2008\04466.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Acura TL Sedan 2012\05748.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Chevrolet Silverado 1500 Hybrid Crew Cab 2012\06396.jpg
C:\Users\uprak\capstone\Car Images\Train Images\Dodge Challenger SRT8 2011\06086.jpg

orig_img_path = list(df_train['Image_Path'])
image = orig_img_path[99]
print(image)
img = cv2.imread(image,cv2.COLOR_BGR2RGB)
print(img.shape)
plt.imshow(img)
plt.show()

C:\Users\uprak\capstone\Car Images\Train Images\Chevrolet Express Van 2007\00100.jpg
(360, 480, 3)

red,green,blue = cv2.split(img)
fig = plt.figure(figsize = (15, 7.2)) 
fig.add_subplot(131)
plt.title("Blue Channel")
plt.imshow(blue)
fig.add_subplot(132)
plt.title("Green Channel")
plt.imshow(green)
fig.add_subplot(133)
plt.title("Red Channel")
plt.imshow(red)
plt.show()

df_blue = blue/255
df_green = green/255
df_red = red/255

from sklearn.decomposition import PCA
from scipy.stats import stats
pca_b = PCA(n_components=40)
pca_b.fit(df_blue)
trans_pca_b = pca_b.transform(df_blue)
pca_g = PCA(n_components=40)
pca_g.fit(df_green)
trans_pca_g = pca_g.transform(df_green)
pca_r = PCA(n_components=40)
pca_r.fit(df_red)
trans_pca_r = pca_r.transform(df_red)

print(trans_pca_b.shape)
print(trans_pca_r.shape)
print(trans_pca_g.shape)

(360, 40)
(360, 40)
(360, 40)

print(f"Blue Channel : {sum(pca_b.explained_variance_ratio_)}")
print(f"Green Channel: {sum(pca_g.explained_variance_ratio_)}")
print(f"Red Channel  : {sum(pca_r.explained_variance_ratio_)}")

Blue Channel : 0.9944291459507676
Green Channel: 0.9943521304638793
Red Channel  : 0.9947486336457025

fig = plt.figure(figsize = (15, 7.2)) 
fig.add_subplot(131)
plt.title("Blue Channel")
plt.ylabel('Variation explained')
plt.xlabel('Eigen Value')
plt.bar(list(range(1,41)),pca_b.explained_variance_ratio_)
fig.add_subplot(132)
plt.title("Green Channel")
plt.ylabel('Variation explained')
plt.xlabel('Eigen Value')
plt.bar(list(range(1,41)),pca_g.explained_variance_ratio_)
fig.add_subplot(133)
plt.title("Red Channel")
plt.ylabel('Variation explained')
plt.xlabel('Eigen Value')
plt.bar(list(range(1,41)),pca_r.explained_variance_ratio_)
plt.show()

b_arr = pca_b.inverse_transform(trans_pca_b)
g_arr = pca_g.inverse_transform(trans_pca_g)
r_arr = pca_r.inverse_transform(trans_pca_r)
print(b_arr.shape, g_arr.shape, r_arr.shape)

(360, 480) (360, 480) (360, 480)

img_reduced= (cv2.merge((r_arr, g_arr, b_arr)))
print(img_reduced.shape)

(360, 480, 3)

fig = plt.figure(figsize = (10, 7.2)) 
fig.add_subplot(121)
plt.title("Original Image")
plt.imshow(img)
fig.add_subplot(122)
plt.title("Reduced Image")
plt.imshow(img_reduced)
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).